DEPARTMENT OF MATHEMATICAL SCIENCES

APPLIED AND COMPUTATIONAL MATHEMATICS SEMINAR

**Speaker:** Yoshitaka Saiki, Hitotsubashi University, Japan

**Title: ***
Machine-learning construction of a model for a macroscopic fluid
variable using the delay-coordinate of a scalar observable
*

**Abstract:**
We construct a data-driven dynamical system model for a macroscopic variable
of a high-dimensionally chaotic fluid flow by training its scalar
time-series data.
We use a machine-learning approach, the reservoir computing for the
construction of the model,
and do not use the knowledge of a physical process of fluid dynamics
in its procedure.
It is confirmed that an inferred time-series obtained from the model
approximates the actual one
and that some characteristics of the chaotic invariant set mimic the
actual ones.
We investigate the appropriate choice of the delay-coordinate,
especially the delay-time and the dimension, which enables us to
construct a model
having a relatively high-dimensional attractor easily.
This is the joint work with Kengo Nakai (University of Tokyo).

**Time:** Friday, February 1, 2019, 1:30-2:30pm

**Place:** Exploratory Hall, Room 4106

Department of Mathematical Sciences

George Mason University

4400 University Drive, MS 3F2

Fairfax, VA 22030-4444

http://math.gmu.edu/

Tel. 703-993-1460, Fax. 703-993-1491